Gaffeltruckar är motordrivna fordon som används flitigt inom industrin för att transportera gods. Automatiseringen av dessa är ett steg i linje med digitaliseringen av industrin och ett prioriterat säkerhetsarbete, och då semiautonoma truckar anses säkrare ökar både utbud och efterfrågan på dessa. Syftet med denna rapport var att undersöka hur företag involverar användare samt integrerar säkerhetsaspekter i sin produktutvecklingsprocess av semiautonoma gaffeltruckar för att minimera risken för personskador. Även synen på automatisering bland användare och tillverkare studerades, samt vilka möjligheter och utmaningar som automatiseringen medför. Forskningsfrågorna besvarades med hjälp av en litteraturstudie och en intervjustudie med fem respondenter från tre olika företag. Resultaten från intervjustudien sammanställdes och jämfördes med den teoretiska referensramen i en efterföljande analys och diskussion. Studien visade att säkerhetsaspekterna beaktas och integreras systematiskt i början av produktutvecklingsprocessen. Tillverkarna såväl som användarna ansåg att säkerhet är en allt viktigare aspekt som upplevs ha blivit mycket bättre under bara de senaste tio åren och säkerhetsaspekterna har utvecklats till att bli mer av hygienfaktorer som måste uppnås på grund av lagkrav och regleringar. En upptäckt som gjordes var därför att användare i regel inte involveras i syfte att integrera säkerhetsfunktioner utan snarare för att möta behov som är svårare att definiera och som handlar om hur produkten ska kännas och upplevas vid användning; såsom körkänsla och ergonomi. Det finns goda möjligheter att öka säkerheten med hjälp av automatisering av gaffeltruckar. Många tillbud och olyckor uppstår på grund av mänskliga faktorer, såsom missförstånd och ouppmärksamhet. Att reducera eller eliminera den mänskliga delen skulle troligen bidra till färre problematiska situationer och misstag, vilket kan rädda liv. Andra möjligheter är kopplade till produktivitet, där det bland annat finns ekonomiska vinster att hämta när industrin effektiviseras. Samtidigt finns det alltid utmaningar i samband med stora digitala omställningar. Automatiseringen av materialhanteringen innebär en investering som behöver godkännas och genomföras, vilket även kräver förarbete och efterforskning. Just bristande kunskap var något som framhävdes som ett hinder, men varken bland användare eller tillverkare ansågs hindrena större än möjligheterna. / Forklifts are motor-driven vehicles which are frequently used for the transportation of goods in the industry. The automation of these is a step in the right direction when it comes to digitalization and prioritized safety work, and as semi-autonomous forklifts are regarded as safer, both the supply and the demand for them are increasing. The aim of this report was to investigate how companies involve users and integrate safety aspects in their product development process of semi-autonomous forklifts to minimize the risk for bodily injuries. Furthermore, the view on automation among users and manufacturers was studied, along with the possibilities and challenges automation entails. The research questions were answered through a literature study and an interview study with five respondents from three different companies. The results from the interview study were compiled and compared with the theoretical source in a subsequent analysis and discussion. The study showed that safety aspects are regarded and integrated systematically during the start of the product development process. The manufacturers as well as the users believed that safety is an aspect which is becoming more and more important and which is perceived as having improved immensely during the last ten years. The safety aspects have evolved into more of hygiene factors which need to be reached due to legislative demands and regulations. A finding from this study was therefore that users were not involved with the purpose to integrate safety functions but rather to meet demands which are more difficult to define and which concern how the product should feel and be perceived in usage, such as driving sense and ergonomics. There are good possibilities to increase safety by automation. Many mishaps and accidents originate from human factors, such as misunderstandings and lack of attention. Reducing or eliminating the human contribution would probably lead to fewer problematic situations and mistakes, which could save lives. Other possibilities are in productivity, where there are financial profits to gain when streamlining the industry. However, there are always challenges related to large, digital transitions. Automation of the material handling means an investment which needs to be approved and realized, which also requires preparatory work and research. Lack of knowledge was a suggested obstacle, but neither the users nor the manufacturers considered the obstacles larger than the possibilities.

The fashion industry is a significant contributor to greenhouse gas emissions and textile waste, prompting the need for sustainable practices. This thesis explores the use of diffusion models for generating synthetic data to enhance datasets used in machine learning, specifically focusing on second-hand fashion. Diffusion models, known for their ability to create high-quality images, offer potential solutions to the imbalance and quality issues in existing datasets. The study investigates how image generation and editing through diffusion models can improve datasets, the effectiveness of different prompting strategies, and the performance of synthetic data in machine learning models compared to real data. The methodology involves using the Kandinsky 2.2 inpainting model to generate and edit images, followed by manual and automated classification to evaluate image quality. Experiments demonstrate that diffusion models can plausibly improve dataset quality by adding and removing damage in images, although fully automating this process remains challenging. The results indicate that augmenting the datasets with synthetic images can potentially enhance the performance of the model, although the variability of the results suggests the need for further research. This thesis contributes to the field of sustainable fashion by proposing innovative methods for dataset augmentation using state-of-the-art generative models, aiming to support the development of efficient and automated sorting processes in the textile industry.

Modern autonomous systems should be able to interact with their surroundings in a flexible yet safe manner. To guarantee this behavior, such systems must learn how to approach unseen entities in their environment through the inference of relationships between actions and objects, called affordances. This research project introduces a neuro-symbolic AI system capable of inferring affordances using attribute detection and knowledge representation as its core principles. The attribute detection module employs a visuo-lingual image captioning model to extract the key object attributes of a scene, while the cognitive knowledge module infers the affordances of those attributes using conditional probability. The practical capabilities of the neuro-symbolic AI system are assessed by implementing a simulated robot system that interacts within the problem space of jars and bottles. The neuro-symbolic AI system is evaluated through its caption-inferring capabilities using image captioning and machine translation metrics. The scores registered in the evaluation show a successful attribute captioning rate of more than 71%. The robot simulation is evaluated within a Unity virtual environment by interacting with 50 jars and bottles, equally divided between lifting and twisting affordances. The robot system successfully interacts with all the objects in the scene due to the robustness of the architecture but fails in the inference process 24 out of the 50 iterations. Contrary to previous works approaching the problem as a classification task, this study shows that affordance inference can be successfully implemented using a cognitive visuo-lingual method. The study’s results justify further study into the use of neuro-symbolic AI approaches to affordance inference.

This thesis explores and evaluates solutions for the inspection of cutting tool containers at Sandvik Coromant, focusing on the transition from current vision systems utilizing infrared (IR) light to new methods compatible with recycled polypropylene (PP) plastic containers. The primary goal is to evaluate the effectiveness of stereo vision and artificial intelligence (AI) for depth estimation, ensuring that the containers are properly populated with cutting tools. Various methods and algorithms are tested to determine their accuracy and speed, to meet the time requirements of the production line at Sandvik Coromant. The results indicate that, while traditional IR-based systems excel in processing speed and robustness, monocular artificial intelligence methods offer adaptability that could be utilized with the new container material. Future work will involve further optimization and real-world testing to confirm these findings.

Målet med examensarbetet har varit att samla information åt ett tekniskt konsultföretag för att öka deras kunskap om autonoma system och fordonskommunikation. Statusen på arbetet kring dessa aktiva säkerhetssystem hos olika aktörer och hur systemen implementeras i dagens och framtidens fordon har undersökts genom omfattande litteraturstudier, intervjuer och marknadsanalyser. De autonoma systemen kan samla information från omgivningen genom sensorer och bidra till ett jämnare trafikflöde, ökad säkerhet, lättare bilar och bättre miljö. Genom fordonskommunikationen kan fordon kommunicera med varandra samt infrastrukturen och garantera en säker bilfärd. År 2030 utgörs innerstaden av autonom, elektrifierad kollektivtrafik för att transportera människor på begäran, samtidigt som personbilar till viss del förbjuds. Potentiella behov för människan i en fullt autonom bil har identifierats och diverse produktutvecklingsmetoder har tillämpats för att utforma två konceptuella lösningar för en framtida bilinteriör. Lösningarna visar interaktionen mellan människa och system eftersom underhållning och bekvämlighet blir viktigt i en fullt autonom bil. Respektive lösning är statsägd och rymmer fyra passagerare. I lösningarna är sittplatserna placerade på ett sätt som underlättar kommunikation mellan passagerarna. Passagerarna kan underhållas eller informeras individuellt eller gemensamt via text, ljud och bild. / The goal with this thesis project has been to collect information for a technical consulting company in order to increase their knowledge about autonomous systems and vehicular communication. The status of how various operators work with active safety systems and how the systems are implemented in current and future vehicles has been investigated through extensive literature studies, interviews and market research. The autonomous systems can collect information from the surrounding through sensors and contribute to better traffic efficiency, increased safety, lighter cars and a better environment. Through vehicle communication, the vehicle can communicate with each other in order to guarantee a safe ride. In 2030 the inner city constitutes of autonomous, electrified public transport to transport people on demand, meanwhile private cars are prohibited. Potential needs for the human in a fully, autonomous car has been identified and various product development methods has been applied in order to develop two conceptual solutions for a future car interior. The solutions show the interaction between human and system since entertainment and comfort becomes important in a fully, autonomous car. Each solution is state-owned and holds four passengers. In the solutions, the seats are placed in regard to facilitate communication between the passengers. The passengers can be entertained or informed individually or collectively by text, sound and images.

In this work, the possibility of realising object tracking by implementing predictive methods with static object detectors is explored. The static object detectors are obtained as models trained on a machine learning algorithm, or in other words, a deep neural network. Specifically, it is the single shot detector inception v2 network that will be used to train such models. Predictive methods will be incorporated to the end of improving the obtained models’ precision, i.e. their performance with respect to accuracy. Namely, Lagrangian mechanics will be employed to derived equations of motion for three different scenarios in which the object is to be tracked. These equations of motion will be implemented as predictive methods by discretising and combining them with four different iterative formulae. In ch. 1, the fundamentals of supervised machine learning, neural networks, convolutional neural networks as well as the workings of the single shot detector algorithm, approaches to hyperparameter optimisation and other relevant theory is established. This includes derivations of the relevant equations of motion and the iterative formulae with which they were implemented. In ch. 2, the experimental set-up that was utilised during data collection, and the manner by which the acquired data was used to produce training, validation and test datasets is described. This is followed by a description of how the approach of random search was used to train 64 models on 300×300 datasets, and 32 models on 512×512 datasets. Consecutively, these models are evaluated based on their performance with respect to camera-to-object distance and object velocity. In ch. 3, the trained models were verified to possess multi-scale detection capabilities, as is characteristic of models trained on the single shot detector network. While the former is found to be true irrespective of the resolution-setting of the dataset that the model has been trained on, it is found that the performance with respect to varying object velocity is significantly more consistent for the lower resolution models as they operate at a higher detection rate. Ch. 3 continues with that the implemented predictive methods are evaluated. This is done by comparing the resulting deviations when they are let to predict the missing data points from a collected detection pattern, with varying sampling percentages. It is found that the best predictive methods are those that make use of the least amount of previous data points. This followed from that the data upon which evaluations were made contained an unreasonable amount of noise, considering that the iterative formulae implemented do not take noise into account. Moreover, the lower resolution models were found to benefit more than those trained on the higher resolution datasets because of the higher detection frequency they can employ. In ch. 4, it is argued that the concept of combining predictive methods with static object detectors to the end of obtaining an object tracker is promising. Moreover, the models obtained on the single shot detector network are concluded to be good candidates for such applications. However, the predictive methods studied in this thesis should be replaced with some method that can account for noise, or be extended to be able to account for it. A profound finding is that the single shot detector inception v2 models trained on a low-resolution dataset were found to outperform those trained on a high-resolution dataset in certain regards due to the higher detection rate possible on lower resolution frames. Namely, in performance with respect to object velocity and in that predictive methods performed better on the low-resolution models. / I detta arbete undersöks möjligheten att åstadkomma objektefterföljning genom att implementera prediktiva metoder med statiska objektdetektorer. De statiska objektdetektorerna erhålls som modeller tränade på en maskininlärnings-algoritm, det vill säga djupa neurala nätverk. Specifikt så är det en modifierad version av entagningsdetektor-nätverket, så kallat entagningsdetektor inception v2 nätverket, som används för att träna modellerna. Prediktiva metoder inkorporeras sedan för att förbättra modellernas förmåga att kunna finna ett eftersökt objekt. Nämligen används Lagrangiansk mekanik för härleda rörelseekvationer för vissa scenarion i vilka objektet är tänkt att efterföljas. Rörelseekvationerna implementeras genom att låta diskretisera dem och därefter kombinera dem med fyra olika iterationsformler. I kap. 2 behandlas grundläggande teori för övervakad maskininlärning, neurala nätverk, faltande neurala nätverk men också de grundläggande principer för entagningsdetektor-nätverket, närmanden till hyperparameter-optimering och övrig relevant teori. Detta inkluderar härledningar av rörelseekvationerna och de iterationsformler som de skall kombineras med. I kap. 3 så redogörs för den experimentella uppställning som användes vid datainsamling samt hur denna data användes för att producera olika data set. Därefter följer en skildring av hur random search kunde användas för att träna 64 modeller på data av upplösning 300×300 och 32 modeller på data av upplösning 512×512. Vidare utvärderades modellerna med avseende på deras prestanda för varierande kamera-till-objekt avstånd och objekthastighet. I kap. 4 så verifieras det att modellerna har en förmåga att detektera på flera skalor, vilket är ett karaktäristiskt drag för modeller tränade på entagninsdetektor-nätverk. Medan detta gällde för de tränade modellerna oavsett vilken upplösning av data de blivit tränade på, så fanns detekteringsprestandan med avseende på objekthastighet vara betydligt mer konsekvent för modellerna som tränats på data av lägre upplösning. Detta resulterade av att dessa modeller kan arbeta med en högre detekteringsfrekvens. Kap. 4 fortsätter med att de prediktiva metoderna utvärderas, vilket de kunde göras genom att jämföra den resulterande avvikelsen de respektive metoderna innebar då de läts arbeta på ett samplat detektionsmönster, sparat från då en tränad modell körts. I och med denna utvärdering så testades modellerna för olika samplingsgrader. Det visade sig att de bästa iterationsformlerna var de som byggde på färre tidigare datapunkter. Anledningen för detta är att den insamlade data, som testerna utfördes på, innehöll en avsevärd mängd brus. Med tanke på att de implementerade iterationsformlerna inte tar hänsyn till brus, så fick detta avgörande konsekvenser. Det fanns även att alla prediktiva metoder förbättrade objektdetekteringsförmågan till en högre utsträckning för modellerna som var tränade på data av lägre upplösning, vilket följer från att de kan arbeta med en högre detekteringsfrekvens. I kap. 5, argumenteras det, bland annat, för att konceptet att kombinera prediktiva metoder med statiska objektdetektorer för att åstadkomma objektefterföljning är lovande. Det slutleds även att modeller som erhålls från entagningsdetektor-nätverket är lovande kandidater för detta applikationsområde, till följd av deras höga detekteringsfrekvenser och förmåga att kunna detektera på flera skalor. Metoderna som användes för att förutsäga det efterföljda föremålets position fanns vara odugliga på grund av deras oförmåga att kunna hantera brus. Det slutleddes därmed att dessa antingen bör utökas till att kunna hantera brus eller ersättas av lämpligare metoder. Den mest väsentliga slutsats detta arbete presenterar är att lågupplösta entagninsdetektormodeller utgör bättre kandidater än de tränade på data av högre upplösning till följd av den ökade detekteringsfrekvens de erbjuder.

[pt] A hermenêutica da ideia de revelação é um dos mais autênticos e originais trabalhos teológicos do filósofo francês Paul Ricoeur. As duas propostas fundantes do autor apresentam um conceito de revelação que respeita as idiossincrasias próprias de cada gênero literário da Escritura e ao mesmo tempo dialoga com uma filosofia hermenêutica que proporciona uma autêntica dialética entre as verdades da fé e as verdades da razão. Assim, critica-se um conceito opaco e autoritário de revelação e reivindica-se um conceito polifônico, polissêmico e analógico de revelação. Os elementos teóricos que implementam o conceito ricoeuriano de revelação são: o mundo do texto, a hermenêutica do distanciamento, o discurso como evento e significação, a categoria Poética e a hermenêutica do testemunho. Por meio dessa dialética viva pretende-se libertar o conceito de revelação das fórmulas dogmáticas e reconduzi-la à confissão de fé presentes nos discursos profético, narrativo, prescritivo, sapiencial e hínico. A opacidade maciça do conceito de revelação somente pode ser superada por intermédio de uma hermenêutica de revelação que respeite as expressões polifônicas originárias da revelação nas quais se baseiam a confissão de fé do povo de Deus. / [en] The hermeneutics of the revelation s idea is one of the more authentic and original theological works of the French philosopher Paul Ricoeur. The author s two fundamental propositions present a concept of revelation that respects the idiosyncrasies fit to each literary gender of the Scripture and, at the same time, dialogues with a hermeneutic philosophy that provides an authentic dialectics between the truths of faith and the truths of reason. So, an opaque and authoritarian concept of the revelation is criticized and a polyphonic, polysemic, and analogical concept of revelation is claimed. The theoretical elements that implement the ricoeurian concept of revelation are: the world of the text, the hermeneutics of detachment, the speech as event and meaning, the poetic cathegory and the hermeneutics of the testimony. Through this living dialectics, one pretends to liberate the concept of revelation from dogmatic formulae and bring it back to the confessions of faith present in the prophetic, narrative, prescriptive, sapiential and hymnic speeches. The massive opacity of the concept of revelation can only be overcome through a hermeneutics of revelation that considers the polyphonic expressions that stem from the revelation in which the people of God s confession of faith are based.

There are many clinical situations where magnetic resonance imaging (MRI) is preferable over other imaging modalities, while the major disadvantage is the relatively long scan time. Due to limited resources, this means that not all patients can be offered an MRI scan, even though it could provide crucial information. It can even be deemed unsafe for a critically ill patient to undergo the examination. In MRI, there is a trade-off between resolution, signal-to-noise ratio (SNR) and the time spent gathering data. When time is of utmost importance, we seek other methods to increase the resolution while preserving SNR and imaging time. In this work, I have studied one of the most promising methods for this task. Namely, constructing super-resolution algorithms to learn the mapping from a low resolution image to a high resolution image using convolutional neural networks. More specifically, I constructed networks capable of transferring high frequency (HF) content, responsible for details in an image, from one kind of image to another. In this context, contrast or weight is used to describe what kind of image we look at. This work only explores the possibility of transferring HF content from T1-weighted images, which can be obtained quite quickly, to T2-weighted images, which would take much longer for similar quality. By doing so, the hope is to contribute to increased efficacy of MRI, and reduce the problems associated with the long scan times. At first, a relatively simple network was implemented to show that transferring HF content between contrasts is possible, as a proof of concept. Next, a much more complex network was proposed, to successfully increase the resolution of MR images better than the commonly used bicubic interpolation method. This is a conclusion drawn from a test where 12 participants were asked to rate the two methods (p=0.0016) Both visual comparisons and quality measures, such as PSNR and SSIM, indicate that the proposed network outperforms a similar network that only utilizes images of one contrast. This suggests that HF content was successfully transferred between images of different contrasts, which improves the reconstruction process. Thus, it could be argued that the proposed multi-contrast model could decrease scan time even further than what its single-contrast counterpart would. Hence, this way of performing multi-contrast super-resolution has the potential to increase the efficacy of MRI.

One fundamental task in robotics is random bin-picking, where it is important to be able to detect an object in a bin and estimate its pose to plan the motion of a robotic arm. For this purpose, this thesis work aimed to investigate and evaluate algorithms for 6D pose estimation when the object was given by a CAD model. The scene was given by a point cloud illustrating a partial 3D view of the bin with multiple instances of the object. Two algorithms were thus implemented and evaluated. The first algorithm was an approach based on Point Pair Features, and the second was Fast Global Registration. For evaluation, four different CAD models were used to create synthetic data with ground truth annotations. It was concluded that the Point Pair Feature approach provided a robust localization of objects and can be used for bin-picking. The algorithm appears to be able to handle different types of objects, however, with small limitations when the object has flat surfaces and weak texture or many similar details. The disadvantage with the algorithm was the execution time. Fast Global Registration, on the other hand, did not provide a robust localization of objects and is thus not a good solution for bin-picking.

Bacteria can quickly spread throughout the human body, making certain diseases hard or impossible to cure. In order to understand how the bacteria can initiate and develop into an infection, microfluidic chambers in a lab environment are used as a template of how bacteria reacts to different types of flows. However, accurately tracking the movement of bacteria is a difficult task, where small objects has to be captured with a high resolution and digitally analysed with computationally heavy methods. Popular imaging methods utilise digital holographic microscopy, where three-dimensional movement is captured in two-dimensional images by numerical reconstruction of the diffraction of light. Since numerical reconstructions become computationally heavy when a good accuracy is required, this master's thesis work focus on evaluating the possibility of using convolutional neural networks to quickly and accurately determine the spatial properties of bacteria. By thorough testing and analysis of state of the art and old networks a new network design is presented, designed to eliminate as many imaging issues as possible. We found that there are certain network design choices that help with reducing the overall error of the system, and with a well chosen training set with sensible augmentations, some networks were able to reach a 60% classification accuracy when determining the vertical rotation of the bacteria. Unfortunately, due to the lack of experimental data where the ground-truth is known, not much experimental testing could be performed. However, a few tests showed that images of high quality could be classified within the expected range of vertical rotation. / Bakterier kan snabbt sprida sig genom människokroppen, vilket försvårar starkt möjligheterna att kurera vissa sjukdomar. För att få en inblick i hur bakterier kan initiera och utvecklas till en infektion används som mall laborativa uppställningar med vätskekanaler i mikroskala när man söker förstå hur bakterier reagerar på olika typer av flöden. Att spåra dessa rörelser med god säkerhet är dock en utmaning, då man experimentellt söker fånga små skalor med hög upplösning, som sedan ska analyseras med datorintensiva metoder. Populära avbildningsmetoder använder sig utav digital holografisk mikroskopi, där tredimensionella rörelser kan fångas med hjälp av tvådimensionella bilder genom att numeriskt återskapa ljusets brytningsmönster mot objekten. Eftersom dessa metoder blir beräkningstunga när god säkerhet krävs så utforskar detta examensarbete möjligheterna att utnyttja faltningsnätverk för att snabbt och säkert bestämma vertikalrotationen hos bakterier avbildade med holografi. Genom nogranna tester av moderna samt äldre nätverk så presenteras en ny nätverksdesign, utvecklad i mål med att eliminera så många avbildningsproblem som möjligt. Vi fann att vissa designval vid nätverksutvecklingen kan hjälpa med att reducera klassificeringsfelen givet vårt system, och med en väl utvald träningsmängd med lämpliga justeringar så lyckades vi nå en klassificeringssäkerhet på över 60% med vissa nätverk. På grund av bristande experimentellt data där de riktiga värdena är kända så har ingen utförlig experimentell analys utförts, men några tester på experimentella bilder i god kvalité har visats ge resultat som tyder på en korrekt analys inom den förväntade vertikalrotationen.